Why Web Usage Mining?
In this paper, we will emphasize on
Web usage mining. Reasons are very simple: With the explosion of E-commerce,
the way companies are doing businesses has been changed. E-commerce, mainly
characterized by electronic transactions through Internet, has provided us a
cost-efficient and effective way of doing business. The growth of some E-businesses is astonishing, considering how
E-commerce has made Amazon.com become the so-called “on-line Wal-Mart”. Unfortunately, to most companies, web is
nothing more than a place where transactions take place. They did not realize
that as millions of visitors interact daily with Web sites around the world,
massive amounts of data are being generated. And they also did not realize that
this information could be very precious to the company in the fields of
understanding customer behavior, improving customer services and relationship,
launching target marketing campaigns, measuring the success of marketing
efforts, and so on.
Web usage mining has emerged as the essential tool for realizing more personalized, user-friendly and business-optimal Web services. Advances in data pre-processing, modeling, and mining techniques, applied to the Web data, have already resulted in many successful applications in adaptive information systems, personalization services, Web analytics tools, and content management systems. As the complexity of Web applications and user’s interaction with these applications increases, the need for intelligent analysis of the Web usage data will also continue to grow. Usage patterns discovered through Web usage mining are effective in capturing item-to-item and user-to-user relationships and similarities at the level of user sessions. However, without the benefit of deeper domain knowledge, such patterns provide little insight into the underlying reasons for which such items or users are grouped together. Furthermore, the inherent and increasing heterogeneity of the Web has required Web-based applications to more effectively integrate a variety of types of data across multiple channels and from different sources. Thus, a focus on techniques and architectures for more effective integration and mining of content, usage, and structure data from different sources is likely to lead to the next generation of more useful and more intelligent applications, and more sophisticated tools for Web usage mining that can derive intelligence from user transactions on the Web.
Web usage mining has emerged as the essential tool for realizing more personalized, user-friendly and business-optimal Web services. Advances in data pre-processing, modeling, and mining techniques, applied to the Web data, have already resulted in many successful applications in adaptive information systems, personalization services, Web analytics tools, and content management systems. As the complexity of Web applications and user’s interaction with these applications increases, the need for intelligent analysis of the Web usage data will also continue to grow. Usage patterns discovered through Web usage mining are effective in capturing item-to-item and user-to-user relationships and similarities at the level of user sessions. However, without the benefit of deeper domain knowledge, such patterns provide little insight into the underlying reasons for which such items or users are grouped together. Furthermore, the inherent and increasing heterogeneity of the Web has required Web-based applications to more effectively integrate a variety of types of data across multiple channels and from different sources. Thus, a focus on techniques and architectures for more effective integration and mining of content, usage, and structure data from different sources is likely to lead to the next generation of more useful and more intelligent applications, and more sophisticated tools for Web usage mining that can derive intelligence from user transactions on the Web.
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